Modelling the Efficiency of Transportation Survey Recruitment and Reminder Methods

Modelling the Efficiency of Transportation Survey Recruitment and Reminder Methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modelling the Efficiency of Transportation Survey Recruitment and Reminder Methods Elis Davanzo, Muntahith Mehadil Orvin, Mahmudur Rahman Fatmi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5457598/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Transportation surveys are vital to generating data for informed infrastructure investment decision-making, developing travel demand models, and monitoring progress. By employing diverse recruitment and data collection strategies, it is possible to improve the overall effectiveness and accuracy of the surveys. With this motivation, this paper examines the efficiency of various survey recruitment and reminder methods. The dataset comes from a time-use survey deployed in Province of British Columbia, which utilized mail and self-registration as recruitment methods. In case of reminders, we used several methods including telephone calls, SMS, emails and social media postings. To further identify factors that influence the effectiveness of reminders and no reminders towards survey completion rates, statistical models are developed. Specifically, an ordered logit model is developed to examine households' need for frequent reminders to complete the survey. Additionally, a hazard model is developed to investigate the time it takes for the households to complete the survey without any reminders. Results indicate that among the self-registrants, about 69% of households that received an SMS completed the survey on the same day, with approximately 40.5% finishing within one hour. Results also revealed that self-registrants, older adults and single individuals are more likely to need more frequent communication to finalize the survey. In contrast, without reminders, residents of Metro Vancouver tend to complete surveys more quickly, while full-time workers may require more time. The findings of this study evaluate the effectiveness of various recruitment methods and reminder techniques, aiding in better targeting and communication to improve response rates. Transportation survey activity time use survey recruitment methods survey reminders ordered logit model hazard-based duration model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5457598","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378801074,"identity":"f134f4b9-9709-4ab2-936e-ac7f945cb0b9","order_by":0,"name":"Elis Davanzo","email":"","orcid":"","institution":"BC Ministry of Transportation and Infrastructure","correspondingAuthor":false,"prefix":"","firstName":"Elis","middleName":"","lastName":"Davanzo","suffix":""},{"id":378801075,"identity":"b6d0f646-8bb5-4c40-bcec-f7b839e1e3b7","order_by":1,"name":"Muntahith Mehadil Orvin","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Muntahith","middleName":"Mehadil","lastName":"Orvin","suffix":""},{"id":378801076,"identity":"3f41e7c2-995a-4821-a14b-0d517b1d36f8","order_by":2,"name":"Mahmudur Rahman Fatmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFACHiBmA2LmA0DCwIbBgHgtbAmMDQwGaSRrYThMWItu+9mjm3nKDuczsDE/f3Sj4HzidvYDjB9+4NFidiYv7TbPucOWDWxshs05BrcTd/YkMEv24NNyIMfsNm/bYQMG+QaIlg03gO7kwafl/BuoFjb2j0At58BaGP/g03IDZgsbD8iWA2AtzHhtufHG7Oacc+kGbGw8hbNzDJKNd/YkNkvL4HVYDlBXmbUBPxv7hs85f+xkt7MfPvjxDR4tcMCGYILiZxSMglEwCkYBRQAAGhZM6WR3NhkAAAAASUVORK5CYII=","orcid":"","institution":"University of British Columbia","correspondingAuthor":true,"prefix":"","firstName":"Mahmudur","middleName":"Rahman","lastName":"Fatmi","suffix":""}],"badges":[],"createdAt":"2024-11-15 04:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5457598/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5457598/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88198539,"identity":"f4646850-7e07-44bf-bf3b-da5d934d293c","added_by":"auto","created_at":"2025-08-03 19:01:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":566947,"visible":true,"origin":"","legend":"","description":"","filename":"paperTheefficiencyoftransportationsurveyrecruitmentmethods.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5457598/v1_covered_5ab5e7ec-111a-4688-9b8b-145a7346c8c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modelling the Efficiency of Transportation Survey Recruitment and Reminder Methods","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transportation survey, activity time use survey, recruitment methods, survey reminders, ordered logit model, hazard-based duration model","lastPublishedDoi":"10.21203/rs.3.rs-5457598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5457598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransportation surveys are vital to generating data for informed infrastructure investment decision-making, developing travel demand models, and monitoring progress. 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Results indicate that among the self-registrants, about 69% of households that received an SMS completed the survey on the same day, with approximately 40.5% finishing within one hour. Results also revealed that self-registrants, older adults and single individuals are more likely to need more frequent communication to finalize the survey. In contrast, without reminders, residents of Metro Vancouver tend to complete surveys more quickly, while full-time workers may require more time. 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